Practical7

Treasure Hunt Trace

library(tmap)

## Warning: package 'tmap' was built under R version 3.4.4

library(geojsonio)

## Warning: package 'geojsonio' was built under R version 3.4.4

## 
## Attaching package: 'geojsonio'

## The following object is masked from 'package:base':
## 
##     pretty

#Here's their data:
hunt <- geojson_read("https://www.dropbox.com/s/wa2ip35tcmt93g3/Team7.geojson?raw=1", what = "sp")

## Warning in strptime(x, fmt, tz = "GMT"): unknown timezone 'zone/tz/2018g.
## 1.0/zoneinfo/Europe/London'

#And here's where they went...
tmap_mode("view")

## tmap mode set to interactive viewing

tm_shape(hunt) +
  tm_lines(col = "green", lwd = 4)

## Warning: package 'sf' was built under R version 3.4.4

## Linking to GEOS 3.6.1, GDAL 2.1.3, proj.4 4.9.3

The location of Tube / Rail Stations in London

#You can have this for free, R fans. Reading XML can be a pain in R, you will need the 'layer' information, which is contained in the <name> tag in a KML file...
#install.packages("rgdal")
library(rgdal)
tubestations <- readOGR("https://www.doogal.co.uk/LondonStationsKML.ashx", "London stations with zone information")

## OGR data source with driver: KML 
## Source: "https://www.doogal.co.uk/LondonStationsKML.ashx", layer: "London stations with zone information"
## with 641 features
## It has 2 fields

Treasure Hunt Locations

library(tidyverse)

huntaddresses <- read_csv("https://www.dropbox.com/s/v66l4cx7aia9jlo/huntLocations.csv?raw=1")

Geocode with Google Maps API

#code here lifted directly from - https://gist.github.com/josecarlosgonz/6417633 

#library the required packages
#install.packages("RCurl")
#install.packages("RJSONIO")
#install.packages("plyr")
#install.packages("tidyverse")
library(RCurl)
library(RJSONIO)
library(plyr)
library(tidyverse)

#highlight this whole block and create this function to access the Google Places API
url <- function(address, return.call = "json", sensor = "false") {
  root <- "http://maps.google.com/maps/api/geocode/"
  u <- paste(root, return.call, "?address=", address, "&sensor=", sensor, sep = "")
  return(URLencode(u))
}

#highlight this whole block and create this function to geocode some places just from a random list of treasure hunt locations
geoCode <- function(address,verbose=FALSE) {
  if(verbose) cat(address,"\n")
  u <- url(address)
  doc <- getURL(u)
  x <- fromJSON(doc,simplify = FALSE)
  if(x$status=="OK") {
    lat <- x$results[[1]]$geometry$location$lat
    lng <- x$results[[1]]$geometry$location$lng
    location_type  <- x$results[[1]]$geometry$location_type
    formatted_address  <- x$results[[1]]$formatted_address
    return(c(lat, lng, location_type, formatted_address))
    Sys.sleep(0.5)
  } else {
    return(c(NA,NA,NA, NA))
  }
}

#now use the geoCode() function (which calls the URL function) to geocode our list of places

#for loop to cycle through every treasure hunt location
i=1
for(i in 1:nrow(huntaddresses)){
  # Every nine records, pause 3 seconds so that the API doesn't kick us off...
  if(i %% 9 == 0) Sys.sleep(3)
  #now create a temporary list of useful elements 
  tempdf <- as.list(geoCode(huntaddresses[i,1]))
  #and write these back into our dataframe
  huntaddresses[i,3] <- tempdf[1]
  huntaddresses[i,4] <- tempdf[2]
  huntaddresses[i,5] <- tempdf[4]
}

# rename the columns
names(huntaddresses)  <- c("Location","Points","lat","lon","GoogleAddress")
head(huntaddresses)

## # A tibble: 6 x 5
##   Location                            Points lat   lon   GoogleAddress
##   <chr>                                <int> <lgl> <lgl> <lgl>        
## 1 Picadilly Circus, London                 2 NA    NA    NA           
## 2 Nelson's Column, London                  2 NA    NA    NA           
## 3 Big Ben, London                          2 NA    NA    NA           
## 4 100 Club, Oxford Street, London          5 NA    NA    NA           
## 5 Fabric, Charterhouse Street, London      5 NA    NA    NA           
## 6 The Gherkin, 30 St Mary Axe, London      2 NA    NA    NA

#write a new .csv file to your working directory
write_csv(huntaddresses, "huntaddresses.csv")

Merge City of London Wards into a Single zone

library(sf)
library(plyr)

library(rgdal)
#Download the Ward Boundaries from Moodle
LondonWards <- readOGR("/Volumes/ucfnnap/CASA_GIS and Sc/wk7/LondonWardsBoundaries/LondonWardsNew.shp")

## OGR data source with driver: ESRI Shapefile 
## Source: "/Volumes/ucfnnap/CASA_GIS and Sc/wk7/LondonWardsBoundaries/LondonWardsNew.shp", layer: "LondonWardsNew"
## with 649 features
## It has 7 fields

tmap_mode("view")

## tmap mode set to interactive viewing

qtm(LondonWards)

LondonWardsSF <- st_as_sf(LondonWards)

#cut the city out - this just happens to be the first 25 rows, usefully!
city <- LondonWardsSF[1:25,]
# city$agg  <- 1
LondonWardsSF <- LondonWardsSF[26:649,]

#merge all of the boundaries together so we've got a single object
############### cityagg <- city %>% group_by(city$agg) %>% summarise()
cityagg <- st_union(city)

# #disolve the ward boundaries and just leave the first one as the city of London
# LondonWards_dis <- aggregate(LondonWardsSF, by = LondonWardsSF, FUN = first)
# 
# #merge them back together into a new object

LondonWards_new <- st_union(LondonWardsSF, cityagg)

## Warning: attribute variables are assumed to be spatially constant
## throughout all geometries

plot(LondonWards_new)

qtm(LondonWards_new)

#finally, update the codes for the city of London so that they match your 
LondonWards_new[1,2] <- as.character("E09000001")

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "E09000001"): invalid factor
## level, NA generated

LondonWards_new[1,3] <- as.character("00AA")

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "00AA"): invalid factor
## level, NA generated

LondonWards_new[1,4] <- as.character("City of London")

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "City of London"): invalid
## factor level, NA generated